Unit-i (testing Doc)

SOFTWARE QUALITY ENGINEERING A. Concepts Software Quality Engineering (SQE) is a process that evaluates, assesses, and improves the quality of software. Software quality is often defined as the degree to which software meets requirements for reliability, maintainability, transportability, etc., as contrasted with functional, performance, and interface requirements that are satisfied as a result of software engineering. Quality must be built into a software product during its development to satisfy quality requirements established for it. SQE ensures that the process of incorporating quality in the software is done properly, and that the resulting software product meets the quality requirements. The degree of conformance to quality requirements usually must be determined by analysis, while functional requirements are demonstrated by testing. SQE performs a function complementary to software development engineering. Their common goal is to ensure that a safe, reliable, and quality engineered software product is developed. B. Software Qualities Qualities for which an SQE evaluation is to be done must first be selected and requirements set for them. Some commonly used qualities are reliability, maintainability, transportability, interoperability, testability, usability, reusability, traceability, sustainability, and efficiency. Some of the key ones are discussed below. 1. Reliability Hardware reliability is often defined in terms of the Mean-Time-ToFailure, or MTTF, of a given set of equipment. An analogous notion is useful for software, although the failure mechanisms are different and the mathematical predictions used for hardware have not yet been usefully applied to software. Software reliability is often defined as the extent to which a program can be expected to perform intended functions with required precision over a given period of time. Software reliability engineering is concerned with the detection and correction of errors in the software; even more, it is concerned with techniques to compensate for unknown software errors and for problems in the hardware and data environments in which the software must operate. 2. Maintainability Software maintainability is defined as the ease of finding and correcting errors in the software. It is analogous to the hardware quality of Mean-Time-To-Repair, or MTTR. While there is as yet no way to directly measure or predict software maintainability, there is a significant body of knowledge about

software attributes that make software easier to maintain. These include modularity, self (internal) documentation, code readability, and structured coding techniques. These same attributes also improve sustainability, the ability to make improvements to the software. 3. Transportability Transportability is defined as the ease of transporting a given set of software to a new hardware and/or operating system environment. 4. Interoperability Software interoperability is the ability of two or more software systems to exchange information and to mutually use the exchanged information. 5. Efficiency Efficiency is the extent to which software uses minimum hardware resources to perform its functions. There are many other software qualities. Some of them will not be important to a specific software system, thus no activities will be performed to assess or improve them. Maximizing some qualities may cause others to be decreased. For example, increasing the efficiency of a piece of software may require writing parts of it in assembly language. This will decrease the transportability and maintainability of the software. C. Metrics Metrics are quantitative values, usually computed from the design or code, that measure the quality in question, or some attribute of the software related to the quality. Many metrics have been invented, and a number have been successfully used in specific environments, but none has gained widespread acceptance. D. A Software Quality Engineering Program The two software qualities which command the most attention are reliability and maintainability. Some practical programs and techniques have been developed to improve the reliability and maintainability of software, even if they are not measurable or predictable. The types of activities that might be included in an SQE program are described here in terms of these two qualities. These activities could be used as a model for the SQE activities for additional qualities. 1. Qualities and Attributes An initial step in laying out an SQE program is to select the qualities that are important in the context of the use of the software that is

being developed. For example, the highest priority qualities for flight software are usually reliability and efficiency. If revised flight software can be up-linked during flight, maintainability may be of interest, but considerations like transportability will not drive the design or implementation. On the other hand, the use of science analysis software might require ease of change and maintainability, with reliability a concern and efficiency not a driver at all. After the software qualities are selected and ranked, specific attributes of the software that help to increase those qualities should be identified. For example, Modularity is an attribute that tends to increase both reliability and maintainability. Modular software is designed to result in code that is apportioned into small, self-contained, functionally unique components or units. Modular code is easier to maintain, because the interactions between units of code are easily understood, and low level functions are contained in few units of code. Modular code is also more reliable, because it is easier to completely test a small, self contained unit. Not all software qualities are so simply related to measurable design and code attributes, and no quality is so simple that it can be easily measured. The idea is to select or devise measurable, analyzable, or testable design and code attributes that will increase the desired qualities. Attributes like information hiding, strength, cohesion, and coupling should be considered. 2. Quality Evaluations Once some decisions have been made about the quality objectives and software attributes, quality evaluations can be done. The intent in an evaluation is to measure the effectiveness of a standard or procedure in promoting the desired attributes of the software product. For example, the design and coding standards should undergo a quality evaluation. If modularity is desired, the standards should clearly say so and should set standards for the size of units or components. Since internal documentation is linked to maintainability, the documentation standards should be clear and require good internal documentation. Quality of designs and code should also be evaluated. This can be done as a part of the walkthrough or inspection process, or a quality audit can be done. In either case, the implementation is evaluated against the standard and against the evaluator's knowledge of good software engineering practices, and examples of poor quality in the product are identified for possible correction. 3. Nonconformance Analysis One very useful SQE activity is an analysis of a project's nonconformance records. The nonconformances should be analyzed for unexpectedly high numbers of events in specific

sections or modules of code. If areas of code are found that have had an unusually high error count (assuming it is not because the code in question has been tested more thoroughly), then the code should be examined. The high error count may be due to poor quality code, an inappropriate design, or requirements that are not well understood or defined. In any case, the analysis may indicate changes and rework that can improve the reliability of the completed software. In addition to code problems, the analysis may also reveal software development or maintenance processes that allow or cause a high proportion of errors to be introduced into the software. If so, an evaluation of the procedures may lead to changes, or an audit may discover that the procedures are not being followed. 4. Fault Tolerance Engineering For software that must be of high reliability, a fault tolerance activity should be established. It should identify software which provides and accomplishes critical functions and requirements. For this software, the engineering activity should determine and develop techniques which will ensure that the needed reliability or fault tolerance will be attained. Some of the techniques that have been developed for high reliability environments include: Input data checking and error tolerance. For example, if out-ofrange or missing input data can affect reliability, then sophisticated error checking and data interpolation/extrapolation schemes may significantly improve reliability. Proof of correctness. For limited amounts of code, formal "proof of correctness" methods may be able to demonstrate that no errors exist. N-Item voting. This is a design and implementation scheme where a number of independent sets of software and hardware operate on the same input. Some comparison (voting) scheme is used to determine which output to use. This is especially effective where subtle timing or hardware errors may be present. Independent development. In this scheme, one or more of the Nitems are independently developed units of software. This helps prevent the simultaneous failure of all items due to a common coding error. E. Techniques and Tools Some of the useful fault-tolerance techniques are described under subsection D, above. Standard statistical techniques can be used to manipulate nonconformance data. In addition, there is considerable experimentation with the Failure Modes and Effects Analysis (FMEA)

technique adapted from hardware reliability engineering. In particular, the FMEA can be used to identify failure modes or other assumable (hardware) system states which can then lead the quality engineer to an analysis of the software that controls the system as it assumes those states. There are also tools that are useful for quality engineering. They include system and software simulators, which allow the modeling of system behavior; dynamic analyzers, which detect the portions of the code that are used most intensively; software tools that are used to compute metrics from code or designs; and a host of special purpose tools that can, for example, detect all system calls to help decide on portability limits. White Box Testing Testing of a function with knowing internal structure of the program. Also known as glass box, structural, clear box and open box testing. A software testing technique whereby explicit knowledge of the internal workings of the item being tested are used to select the test data. Unlike black box testing, white box testing uses specific knowledge of programming code to examine outputs. The test is accurate only if the tester knows what the program is supposed to do. He or she can then see if the program diverges from its intended goal. White box testing does not account for errors caused by omission, and all visible code must also be readable. Black Box Testing Testing of a function without knowing internal structure of the program. Black-box and white-box are test design methods. Black-box test design treats the system as a "black-box", so it doesn't explicitly use knowledge of the internal structure. Black-box test design is usually described as focusing on testing functional requirements. Synonyms for black-box include: behavioral, functional, opaque-box, and closed-box. White-box test design allows one to peek inside the "box", and it focuses specifically on using internal knowledge of the software to guide the selection of test data. Synonyms for white-box include: structural, glass-box and clearbox. While black-box and white-box are terms that are still in popular use, many people prefer the terms "behavioral" and "structural". Behavioral test design is slightly different from black-box test design because the use of internal knowledge isn't strictly forbidden, but it's still discouraged. In practice, it hasn't proven useful to use a single test design method. One has to use a mixture of different methods so that they aren't hindered by

the limitations of a particular one. Some call this "gray-box" or "translucent-box" test design, but others wish we'd stop talking about boxes altogether. It is important to understand that these methods are used during the test design phase, and their influence is hard to see in the tests once they're implemented. Note that any level of testing (unit testing, system testing, etc.) can use any test design methods. Unit testing is usually associated with structural test design, but this is because testers usually don't have well-defined requirements at the unit level to validate. Unit Testing In computer programming, a unit test is a method of testing the correctness of a particular module of source code. The idea is to write test cases for every non-trivial function or method in the module so that each test case is separate from the others if possible. This type of testing is mostly done by the developers. Benefits The goal of unit testing is to isolate each part of the program and show that the individual parts are correct. It provides a written contract that the piece must satisfy. This isolated testing provides four main benefits: Encourages change Unit testing allows the programmer to refactor code at a later date, and make sure the module still works correctly (regression testing). This provides the benefit of encouraging programmers to make changes to the code since it is easy for the programmer to check if the piece is still working properly. Simplifies Integration Unit testing helps eliminate uncertainty in the pieces themselves and can be used in a bottom-up testing style approach. By testing the parts of a program first and then testing the sum of its parts will make integration testing easier. Documents the code Unit testing provides a sort of "living document" for the class being tested. Clients looking to learn how to use the class can look at the unit tests to determine how to use the class to fit their needs. Separation of Interface from Implementation Because some classes may have references to other classes, testing a class can frequently spill over into testing another class. A common example of this is classes that depend on a database; in order to test the class, the tester finds herself writing code that interacts with the

database. This is a mistake, because a unit test should never go outside of its own class boundary. As a result, the software developer abstracts an interface around the database connection, and then implements that interface with their own Mock Object. This results in loosely coupled code, thus minimizing dependencies in the system. Limitations It is important to realize that unit-testing will not catch every error in the program. By definition, it only tests the functionality of the units themselves. Therefore, it will not catch integration errors, performance problems and any other system-wide issues. In addition, it may not be trivial to anticipate all special cases of input the program unit under study may receive in reality. Unit testing is only effective if it is used in conjunction with other software testing activities. Integration Testing It is the phase of software testing in which individual software modules are combined and tested as a group. It follows unit testing and precedes system testing. takes as its input modules that have been checked out by unit testing, groups them in larger aggregates, applies tests defined in an Integration test plan to those aggregates, and delivers as its output the integrated system ready for system testing. Purpose The purpose of Integration testing is to verify functional, performance and reliability requirements placed on major design items. These "design items", i.e. assemblages (or groups of units), are exercised through their interfaces using Black box testing, success and error cases being simulated via appropriate parameter and data inputs. Simulated usage of shared data areas and inter-process communication is tested, individual subsystems are exercised through their input interface. All test cases are constructed to test that all components within assemblages interact correctly, for example, across procedure calls or process activations. The overall idea, is the "building block" approach in which verified assemblages are added to a verified base which is then used to support the Integration testing of further assemblages. Performance testing In software engineering, performance testing is testing that is performed to determine how fast some aspect of a system performs under a particular workload. Performance testing can serve different purposes. It can demonstrate that the system meets performance criteria. It can compare two systems to find which performs better. Or it can measure what parts of the system or

workload cause the system to perform badly. In the diagnostic case, software engineers use tools such as profilers to measure what parts of a device or software contribute most to the poor performance or to establish throughput levels (and thresholds) for maintained acceptable response time. In performance testing, it is often crucial (and often difficult to arrange) for the test conditions to be similar to the expected actual use. Technology Performance testing technology employs one or more PCs to act as injectors – each emulating the presence or numbers of users and each running an automated sequence of interactions (recorded as a script, or as a series of scripts to emulate different types of user interaction) with the host whose performance is being tested. Usually, a separate PC acts as a test conductor, coordinating and gathering metrics from each of the injectors and collating performance data for reporting purposes. The usual sequence is to ramp up the load – starting with a small number of virtual users and increasing the number over a period to some maximum. The test result shows how the performance varies with the load, given as number of users vs response time. Various tools, including Compuware Corporation's QACenter Performance Edition, are available to perform such tests. Tools in this category usually execute a suite of tests which will emulate real users against the system. Sometimes the results can reveal oddities, e.g., that while the average response time might be acceptable, there are outliers of a few key transactions that take considerably longer to complete – something that might be caused by inefficient database queries, etc. Performance testing can be combined with stress testing, in order to see what happens when an acceptable load is exceeded –does the system crash? How long does it take to recover if a large load is reduced? Does it fail in a way that causes collateral damage? Performance specifications Performance testing is frequently not performed against a specification, i.e. no one will have expressed what is the maximum acceptable response time for a given population of users. However, performance testing is frequently used as part of the process of performance profile tuning. The idea is to identify the “weakest link” – there is inevitably a part of the system which, if it is made to respond faster, will result in the overall system running faster. It is sometimes a difficult task to identify which part of the system represents this critical path, and some test tools come provided with (or can have add-ons that provide) instrumentation that runs on the server and reports transaction times, database access times, network overhead, etc. which can be analysed together with the raw performance statistics. Without such instrumentation one might have to have someone crouched over Windows Task Manager at the server to see

how much CPU load the performance tests are generating. There is an apocryphal story of a company that spent a large amount optimising their software without having performed a proper analysis of the problem. They ended up rewriting the system’s ‘idle loop’, where they had found the system spent most of its time, but even having the most efficient idle loop in the world obviously didn’t improve overall performance one iota! Performance testing almost invariably identifies that it is parts of the software (rather than hardware) that contribute most to delays in processing users’ requests. Performance testing can be performed across the web, and even done in different parts of the country, since it is known that the response times of the internet itself vary regionally. It can also be done in-house, although routers would then need to be configured to introduce the lag what would typically occur on public networks. It is always helpful to have a statement of the likely peak numbers of users that might be expected to use the system at peak times. If there can also be a statement of what constitutes the maximum allowable 95 percentile response time, then an injector configuration could be used to test whether the proposed system met that specification. Tasks to undertake Tasks to perform such a test would include: * Analysis of the types of interaction that should be emulated and the production of scripts to do those emulations * Decision whether to use internal or external resources to perform the tests. * set up of a configuration of injectors/controller * set up of the test configuration (ideally identical hardware to the production platform), router configuration, quiet network (we don’t want results upset by other users), deployment of server instrumentation. * Running the tests – probably repeatedly in order to see whether any unaccounted for factor might affect the results. * Analysing the results, either pass/fail, or investigation of critical path and recommendation of corrective action.

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Stress Testing is a form of testing that is used to determine the stability of a given system or entity. It involves testing beyond normal operational capacity, often to a breaking point, in order to observe the results. For example, a web server may be stress tested using scripts, bots, and various denial of service tools to observe the performance of a web site during peak loads. Stress testing a subset of load testing. Also see testing, software testing, performance testing. Security Testing Application vulnerabilities leave your system open to attacks, Downtime, Data theft, Data corruption and application Defacement. Security within an application or web service is crucial to avoid such vulnerabilities and new threats. While automated tools can help to eliminate many generic security issues, the detection of application vulnerabilities requires independent evaluation of your specific application's features and functions by experts. An external security vulnerability review by Third Eye Testing will give you the best possible confidence that your application is as secure as possible. Installation Testing Installation testing (in software engineering) can simply be defined as any testing that occurs outside of the development environment. Such testing will frequently occur on the computer system the software product will eventually be installed on. Whilst the ideal installation might simply appear to be to run a setup program, the generation of that setup program itself and its efficacy in a variety of machine and operating system environments can require extensive testing before it can be used with confidence. Alpha Testing In software development, testing is usually required before release to the general public. In-house developers often test the software in what is known as 'ALPHA' testing which is often performed under a debugger or with hardware-assisted debugging to catch bugs quickly. It can then be handed over to testing staff for additional inspection in an environment similar to how it was intended to be used. This technique is known as black box testing. This is often known as the second stage of alpha testing In distributed systems, particularly where software is to be released into an already live target environment (such as an operational web site) installation (or deployment as it is sometimes called) can involve database schema changes as well as the installation of new software. Deployment

plans in such circumstances may include back-out procedures whose use is intended to roll the target environment back in the event that the deployment is unsuccessful. Ideally, the deployment plan itself should be tested in an environment that is a replica of the live environment. A factor that can increase the organisational requirements of such an exercise is the need to synchronize the data in the test deployment environment with that in the live environment with minimum disruption to live operation. Usability testing is a means for measuring how well people can use some human-made object (such as a web page, a computer interface, a document, or a device) for its intended purpose, i.e. usability testing measures the usability of the object. Usability testing focuses on a particular object or a small set of objects, whereas general humancomputer interaction studies attempt to formulate universal principles. If usability testing uncovers difficulties, such as people having difficulty understanding instructions, manipulating parts, or interpreting feedback, then developers should improve the design and test it again. During usability testing, the aim is to observe people using the product in as realistic a situation as possible, to discover errors and areas of improvement. Designers commonly focus excessively on creating designs that look "cool", compromising usability and functionality. This is often caused by pressure from the people in charge, forcing designers to develop systems based on management expectations instead of people's needs. A designers' primary function should be more than appearance, including making things work with people. "Caution: simply gathering opinions is not usability testing -- you must arrange an experiment that measures a subject's ability to use your document." Rather than showing users a rough draft and asking, "Do you understand this?", usability testing involves watching people trying to use something for its intended purpose. For example, when testing instructions for assembling a toy, the test subjects should be given the instructions and a box of parts. Instruction phrasing, illustration quality, and the toy's design all affect the assembly process. Setting up a usability test involves carefully creating a scenario, or realistic situation, wherein the person performs a list of tasks using the product being tested while observers watch and take notes. Several other test instruments such as scripted instructions, paper prototypes, and preand post-test questionnaires are also used to gather feedback on the product being tested. For example, to test the attachment function of an e-mail program, a scenario would describe a situation where a person needs to send an e-mail attachment, and ask him or her to undertake this task. The aim is to observe how people function in a realistic manner, so that developers can see problem areas, and what people like. The technique popularly used to gather data during a usability test is called a think aloud protocol. Beta Testing

In software development, testing is usually required before release to the general public. In-house developers often test the software in what is known as 'Beta' testing which is often performed under a debugger or with hardware-assisted debugging to catch bugs quickly. It can then be handed over to testing staff for additional inspection in an environment similar to how it was intended to be used. This technique is known as black box testing. This is often known as the second stage of Beta testing. Product Testing Software Product development companies face unique challenges in testing. Only suitably organized and executed test process can contribute to the success of a software product. Product testing experts design the test process to take advantage of the economies of scope and scale that are present in a software product. These activities are sequenced and scheduled so that a test activity occurs immediately following the construction activity whose output the test is intended to validate. Stability Testing In PDF files, stability testing is an attempt to determine if an application will crash. In the pharmaceutical field, it refers to a period of time during which a multi-dose product retains its quality after the container is opened. Acceptance Testing User acceptance testing (UAT) is one of the final stages of a software project and will often occur before the customer accepts a new system. Users of the system will perform these tests which, ideally, developers have derived from the User Requirements Specification, to which the system should conform. Test designers will draw up a formal test plan and devise a range of severity levels. The focus in this type of testing is less on simple problems (spelling mistakes, cosmetic problems) and show stoppers (major problems like the software crashing, software will not run etc.). Developers should have worked out these issues during unit testing and integration testing. Rather, the focus is on a final verification of the required business function and flow of the system. The test scripts will emulate real-world usage of the system. The idea is that if the software works as intended and without issues during a simulation of normal use, it will work just the same in production.

Results of these tests will allow both the customers and the developers to be confident that the system will work as intended. System Testing According to the IEEE Standard Computer Dictionary, System testing is testing conducted on a complete, integrated system to evaluate the system's compliance with its specified requirements. System testing falls within the scope of Black box testing, and as such, should require no knowledge of the inner design of the code or logic (IEEE. IEEE Standard Computer Dictionary: A Compilation of IEEE Standard Computer Glossaries. New York, NY. 1990.). Alpha testing and Beta testing are sub-categories of System testing. As a rule, System testing takes, as its input, all of the "integrated" software components that have successfully passed Integration testing and also the software system itself integrated with any applicable hardware system(s). The purpose of Integration testing is to detect any inconsistencies between the software units that are integrated together called assemblages or between any of the assemblages and hardware. System testing is more of a limiting type of testing, where it seeks to detect both defects within the "inter-assemblages" and also the system as a whole. Regression Testing According to the IEEE Standard Computer Dictionary, Regression testing is testing conducted on a complete, integrated Regression to evaluate the Regression's compliance with its specified requirements. Regression testing falls within the scope of Black box testing, and as such, should require no knowledge of the inner design of the code or logic (IEEE. IEEE Standard Computer Dictionary: A Compilation of IEEE Standard Computer Glossaries. New York, NY. 1990.). Alpha testing and Beta testing are sub-categories of Regression testing. As a rule, Regression testing takes, as its input, all of the "integrated" software components that have successfully passed Integration testing and also the software Regression itself integrated with any applicable hardware Regression(s). The purpose of Integration testing is to detect any inconsistencies between the software units that are integrated together called assemblages or between any of the assemblages and hardware. Regression testing is more of a limiting type of testing, where it seeks to detect both defects within the "inter-assemblages" and also the Regression as a whole

Compatibility Testing One of the challenges of software development is ensuring that the application works properly on the different platforms and operating systems on the market and also with the applications and devices in its environment. Compatibility testing service aims at locating application problems by running them in real environments, thus ensuring you that your application is compatible with various hardware, operating system and browser versions. Fuzz testing Fuzz testing is a software testing technique. The basic idea is to attach the inputs of a program to a source of random data. If the program fails (for example, by crashing, or by failing in-built code assertions), then there are defects to correct. The great advantage of fuzz testing is that the test design is extremely simple, and free of preconceptions about system behavior. Uses Fuzz testing is often used in large software development projects that perform black box testing. These usually have a budget to develop test tools, and fuzz testing is one of the techniques which offers a high benefit:cost ratio. Fuzz testing is also used as a gross measurement of a large software system's quality. The advantage here is that the cost of generating the tests is relatively low. For example, third party testers have used fuzz testing to evaluate the relative merits of different operating systems and application programs. Fuzz testing is thought to enhance software security and software safety because it often finds odd oversights and defects which human testers would fail to find, and even careful human test designers would fail to create tests for. However, fuzz testing is not a substitute for exhaustive testing or formal methods: it can only provide a random sample of the system's behavior, and in many cases passing a fuzz test may only demonstrate that a piece of software handles exceptions without crashing, rather than behaving correctly. Thus, fuzz testing can only be regarded as a proxy for program correctness, rather than a direct measure, with fuzz test failures actually being more useful as a bug-finding tool than fuzz test passes as an assurance of quality. Fuzz testing methods As a practical matter, developers need to reproduce errors in order to fix them. For this reason, almost all fuzz testing makes a record of the data it

manufactures, usually before applying it to the software, so that if the computer fails dramatically, the test data is preserved. Modern software has several different types of inputs: * Event driven inputs are usually from a graphical user interface, or possibly from a mechanism in an embedded system. * Character driven inputs are from files, or data streams. * Database inputs are from tabular data, such as relational databases. There are at least two different forms of fuzz testing: * Valid fuzz attempts to assure that the random input is reasonable, or conforms to actual production data. * Simple fuzz usually uses a pseudo random number generator to provide input. * An combined approach uses valid test data with some proportion of totally random input injected. By using all of these techniques in combination, fuzz-generated randomness can test the un-designed behavior surrounding a wider range of designed system states. Fuzz testing may use tools to simulate all of these domains. Event-driven fuzz Normally this is provided as a queue of datastructures. The queue is filled with data structures that have random values. The most common problem with an event-driven program is that it will often simply use the data in the queue, without even crude validation. To succeed in a fuzz-tested environment, software must validate all fields of every queue entry, decode every possible binary value, and then ignore impossible requests. One of the more interesting issues with real-time event handling is that if error reporting is too verbose, simply providing error status can cause resource problems or a crash. Robust error detection systems will report only the most significant, or most recent error over a period of time. Character-driven fuzz Normally this is provided as a stream of random data. The classic source in UNIX is the random data generator. One common problem with a character driven program is a buffer overrun, when the character data exceeds the available buffer space. This

problem tends to recur in every instance in which a string or number is parsed from the data stream and placed in a limited-size area. Another is that decode tables or logic may be incomplete, not handling every possible binary value. Database fuzz The standard database scheme is usually filled with fuzz that is random data of random sizes. Some IT shops use software tools to migrate and manipulate such databases. Often the same schema descriptions can be used to automatically generate fuzz databases. Database fuzz is controversial, because input and comparison constraints reduce the invalid data in a database. However, often the database is more tolerant of odd data than its client software, and a general-purpose interface is available to users. Since major customer and enterprise management software is starting to be open-source, database-based security attacks are becoming more credible. A common problem with fuzz databases is buffer overrun. A common data dictionary, with some form of automated enforcement is quite helpful and entirely possible. To enforce this, normally all the database clients need to be recompiled and retested at the same time. Another common problem is that database clients may not enderstand the binary possibilities of the database field type, or, legacy software might have been ported to a new database system with different possible binary values. A normal, inexpensive solution is to have each program validate database inputs in the same fashion as user inputs. The normal way to achieve this is to periodically "clean" production databases with automated verifiers.